Deep Learning: GANs and Variational Autoencoders

Ever wondered how AI technologies like OpenAI DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently.Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs.GAN stands for generative adversarial network, where 2 neural networks compete with each other.What is unsupervised learning?Unsupervised learning means we’re not trying to map input data to targets, we’re just trying to learn the structure of that input data.Once we’ve learned that structure, we can do some pretty cool things.One example is generating poetry - we’ve done examples of this in the past.But poetry is a very specific thing, how about writing in general?If we can learn the structure of language, we can generate any kind of text. In fact, big companies are putting in lots of money to research how the news can be written by machines.But what if we go back to poetry and take away the words?Well then we get art, in general.By learning the structure of art, we can create more art.How about art as sound?If we learn the structure of music, we can create new music.Imagine the top 40 hits you hear on the radio are songs written by robots rather than humans.The possibilities are endless!You might be wondering, "how is this course different from the first unsupervised deep learning course?"In this first course, we still tried to learn the structure of data, but the reasons were different.We wanted to learn the structure of data in order to improve supervi